António Bernardo Ferreira

Abstract

Abstract
The planning of mining operations in water filled open-pit mines requires detailed bathymetry to create a mine plan and assess the involved risks. This paper presents postprocessing techniques for creating an improved 3D model from a survey carried out using an autonomous surface vehicle with a multibeam sonar and a GPS/INS navigation system. Inconsistencies of the created point cloud as a result of calibration errors or GPS signal loss are corrected using a continuous-time simultaneous localization and mapping (SLAM) solution. Signed distance function (SDF) based mapping is employed to fuse the measurements from multiple runs into a consistent representation and reduce sensor noise. From the signed distance function model we reconstruct a 3D surface mesh. We use this terrain model to establish a virtual reality scene for immersive data visualization of the mining operations for testing and planing during development. Results of the proposed approach are demonstrated on a dataset captured in an abandoned submerged inland mine. © 2017

Abstract
This paper addresses a three-dimensional (3D) reconstruction of a flooded open pit mine with an autonomous surface vehicle (ASV) and unmanned aerial vehicle (UAV). The ROAZ USV and the Otus UAV were used to provide the underwater bathymetric map and aerial 3D reconstruction based from image data. This work was performed within the context of the European research project VAMOS with the objective of developing robotic tools for efficient underwater mining © 2016 IEEE.

Abstract
A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. Themethod aims to identify shipwrecked people by recognizing the hand waving gesture sign. We introduce a novelmotion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously. The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

Abstract